The changes in the model architecture include using local sliding window attention and global attention alternately, incorporating logit soft capping to prevent logit blow up during training, adding post and pre-attention RMS norm for stability, and utilizing group query attention GQA with varying numbers of KV heads for different models. The 27B model is trained on around 13 trillion tokens, the 9B on 8 trillion tokens, and there are plans for a 2.6B model trained on 2 trillion tokens.

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